Ideally, wireless networks should be optimized to deliver the best Quality of Service (in terms of reliability, delay, and throughput) to subscribers with the minimum expenditure in resources. Such resources include transmitted power, transmitter and receiver complexity, and allocated frequency spectrum. Over the last few years, there has been an ever increasing demand for wireless spectrum resulting from the adoption of throughput hungry applications in a variety of civilian, military, and scientific settings. Because the available spectrum is non-renewable and limited, this demand motivates the need for efficient wireless networks that maximally utilize the spectrum. Although there is a severe shortage in the spectrum, it is well-documented now that a significant fraction of the available spectrum is under-utilized [1]. This, in fact, is the main motivation for the cognitive networking framework where secondary users are allowed to use the spectrum in the off time, where the primary users are idle, in an attempt to maximize the spectral efficiency [2]. Unfortunately, the cognitive radio approach is still facing significant regulatory and technological hurdles [3], [4] and, at best, offers only a partial solution to the problem.
This limitation of the cognitive radio approach is intimately tied to the main reason behind the under-utilization of the spectrum; namely the large disparity between the average and peak traffic demand in the network. As an example, in a typical cellular network, one can easily see that the traffic demand in the peak hours is much higher than that at night, which inspires the different rates offered by cellular operators. Now, the cognitive radio approach assumes that the secondary users will be able to utilize the spectrum in the off-peak times but, unfortunately, at those particular times one may expect the secondary traffic characteristics to be similar to that of the primary users (e.g., at night most of the primary and secondary users are expected to be idle).
In the traditional approach, wireless networks are constructed assuming that the subscribers are equipped with dumb terminals with very limited computational power. It is obvious that the new generation of smart devices enjoy significantly enhanced capabilities in terms of both processing power and available memory. Moreover, according to Moore's law predictions, one should expect the computational and memory resources available at the typical wireless device to increase at an exponential rate. This observation should inspire a similar paradigm shift in the design of wireless networks whereby the capabilities of the smart wireless terminals are leveraged to maximize the utility of the frequency spectrum, a non-renewable resource that does not scale according to Moore's law.
The introduction of smart phones has resulted in a paradigm shift in the dominant traffic in mobile cellular networks. While the primary traffic source in traditional cellular networks was real-time voice communication, one can argue that a significant fraction of the traffic generated by the smart phones results from non-data-requests (e.g., file downloads). This feature allows for more degrees of freedom in the design of a scheduling algorithm.
There is a need for a new approach to the resource allocation aspect of the problem and a new paradigm that offers spectral gains in a variety of relevant scenarios. More specifically, there is a need for a proactive resource allocation framework that exploits the predictability of daily usage of wireless devices to smooth out the traffic demand in the network, and hence, reduce the required resources to achieve a certain point on the Quality of Service (QoS) curve.